मैंने AWS EC2 गाइड का उपयोग करके स्पार्क स्थापित किया और मैं bin/pysparkस्पार्क प्रॉम्प्ट को प्राप्त करने के लिए स्क्रिप्ट का उपयोग करके प्रोग्राम को ठीक से लॉन्च कर सकता हूं और क्विक स्टार्ट क्वाइड को सफलतापूर्वक भी कर सकता हूं।
हालाँकि, मैं अपने जीवन के लिए यह पता नहीं लगा सकता कि INFOप्रत्येक कमांड के बाद सभी वर्बोज़ लॉगिंग को कैसे रोका जाए ।
मैंने नीचे दिए गए कोड में लगभग हर संभावित परिदृश्य की कोशिश की है (बाहर टिप्पणी करना, बंद करना) फ़ोल्डर में अपनी log4j.propertiesफ़ाइल के भीतर confजहां मैं एप्लिकेशन को और साथ ही प्रत्येक नोड पर लॉन्च करता हूं और कुछ भी नहीं कर रहा हूं। मुझे अभी भी INFOप्रत्येक कथन को निष्पादित करने के बाद लॉगिंग स्टेटमेंट प्रिंटिंग मिलती है ।
मैं बहुत उलझन में हूँ कि यह कैसे काम करने वाला है।
#Set everything to be logged to the console log4j.rootCategory=INFO, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
# Settings to quiet third party logs that are too verbose
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
जब मैं उपयोग करता हूं तो यहां मेरा पूरा क्लासपाथ है SPARK_PRINT_LAUNCH_COMMAND:
स्पार्क कमांड: /Library/Java/JavaVirtualMachines/jdk1.8.0_05.jdk/Contents/Home/bin/java -cp: /root/spark-1.1-bin-hadoop2/conf: /root/spark-1.0.1.1 -bin-hadoop2 / conf: /root/spark-1.0.1-bin-hadoop2/lib/spark-assembly-1.0.1-hadoop2.2.0.jar: /root/spark-1.0.1-bin-hadoop2/lib /datanucleus-api-jdo-3.2.1.jar:/root/spark-1.0.1-bin-hadoop2/lib/datanucleus-core-3.2.2.jar:/root/spark-1.0.1-bin-hadoop2 /lib/datanucleus-rdbms-3.2.1.jar -XX: MaxPermSize = 128m -Djava.library.path = -Xms512m -Xmx512m org.apache .spark.deploy.SparkSubmit स्पार्क-शेल --class org.apache.spark.spark। repl.Main
की सामग्री spark-env.sh:
#!/usr/bin/env bash
# This file is sourced when running various Spark programs.
# Copy it as spark-env.sh and edit that to configure Spark for your site.
# Options read when launching programs locally with
# ./bin/run-example or ./bin/spark-submit
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - SPARK_PUBLIC_DNS, to set the public dns name of the driver program
# - SPARK_CLASSPATH=/root/spark-1.0.1-bin-hadoop2/conf/
# Options read by executors and drivers running inside the cluster
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program
# - SPARK_CLASSPATH, default classpath entries to append
# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data
# - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos
# Options read in YARN client mode
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2)
# - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1).
# - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G)
# - SPARK_DRIVER_MEMORY, Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)
# - SPARK_YARN_APP_NAME, The name of your application (Default: Spark)
# - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’)
# - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job.
# - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job.
# Options for the daemons used in the standalone deploy mode:
# - SPARK_MASTER_IP, to bind the master to a different IP address or hostname
# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master
# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")
# - SPARK_WORKER_CORES, to set the number of cores to use on this machine
# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker
# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
# - SPARK_WORKER_DIR, to set the working directory of worker processes
# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers
export SPARK_SUBMIT_CLASSPATH="$FWDIR/conf"